Regret minimization has also been studied in auctions, with two prominent lines of work focusing on the econometric problem of inferring structural parameters of the utilities of the bidders
[1]}, [2]}, [3]}, [4]},
and on the price of anarchy in classical auction formats and combinatorial auctions when the bidders are no-regret learners [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}.
Other works deal with the design of automated bidding algorithms for auctions with more complex factors, such as budget constraints [12]}, [13]}, or the utilization of regret minimization ideas for optimizing reserve prices [14]}, [15]}, [16]}, and in [17]}, [18]} regret minimization algorithms were used for predicting bidder behavior. Importantly, [1]}, [17]} provide concrete empirical evidence from large ad auction datasets showing that actual bidder behavior is consistent with no-regret learning.
